The present invention relates generally to gas turbines, and more particularly to a control system for a gas turbine that includes a neural network for determining certain turbine operating parameters of importance for effective control of the gas turbine.
Gas turbines are commonly used as a power source to drive electrical generating equipment (such as in an electrical power station) or for propulsion (e.g., for aircraft, marine vessels, or military equipment such as tanks). A combustion type gas turbine has a gas path which typically includes, in a serial-flow relationship, an air intake (or inlet), a compressor, a combustor, a turbine, and a gas outlet (or exhaust nozzle). Control of the power generated by the gas turbine is typically exercised through control of fuel flow and air flow into the combustor.
Efficient operation of the gas turbine--e.g., to obtain desired fuel economy, to establish and maintain a desired power output that is stable within defined limits, and to reduce the level of emissions--requires that a number of critical turbine operating parameters be processed to determine optimal settings for controllable parameters, such as fuel flow and distribution, and intake air flow. Examples of such turbine operating parameters include compressor inlet and outlet temperatures and pressures, exhaust temperatures and pressures, and the like.
Certain turbine operating parameters cannot be directly measured reliably; hence estimates of the values of such parameters are made based on the available sensor data. One example of an important operating parameter in a power-generation gas turbine that cannot be directly measured is the combustion reference temperature (TTRF). While not representing a particular physical temperature in the turbine, this parameter is nevertheless an important control variable that governs a number of critical functions. For example, dynamically the TTRF correlates well with the average fuel-air-ratio in the combustor and hence is used to control the division of fuel going to various parts of the combustor. Efficient operation of the turbine requires that accurate information about this parameter be available to the controller at all times.
Calculation of values of TTRF conventionally is achieved by processing selected sensor measurements through a model having simplified aero-thermal equations for the turbine. The model for TTRF is usually represented in the controller as a set of nonlinear equations, with sensed parameters such as the compressor discharge pressure, turbine exhaust temperature, exhaust air flow, ambient temperature, and guide vane angle, serving as inputs. Because the modeled system is nonlinear, even complex models can estimate this parameter only over a limited range of operating points. Even so, estimation of this parameter requires significant computing power given the number of mathematical steps that must be completed to make the estimate; the time taken to accomplish these processing steps further places limitations on conventional controllers.
It is desirable that a gas turbine controller provide effective control over a wide range of turbine operating conditions. To achieve such performance, an effective controller advantageously is adapted to rapidly and accurately generate values for calculated (or modeled) turbine operating parameters over the wide range of turbine performance.